Overview

Dataset statistics

Number of variables16
Number of observations1121825
Missing cells195100
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory136.9 MiB
Average record size in memory128.0 B

Variable types

Numeric12
Categorical4

Alerts

time has a high cardinality: 45093 distinct values High cardinality
gameId is highly correlated with teamHigh correlation
frameId is highly correlated with s and 1 other fieldsHigh correlation
s is highly correlated with disHigh correlation
a is highly correlated with sHigh correlation
dis is highly correlated with sHigh correlation
team is highly correlated with gameIdHigh correlation
nflId has 48775 (4.3%) missing values Missing
jerseyNumber has 48775 (4.3%) missing values Missing
o has 48775 (4.3%) missing values Missing
dir has 48775 (4.3%) missing values Missing
s has 69592 (6.2%) zeros Zeros
a has 65085 (5.8%) zeros Zeros
dis has 70615 (6.3%) zeros Zeros

Reproduction

Analysis started2022-11-02 15:05:19.120450
Analysis finished2022-11-02 15:06:59.168554
Duration1 minute and 40.05 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

gameId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021092594
Minimum2021092300
Maximum2021092700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 MiB
2022-11-02T12:06:59.215605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2021092300
5-th percentile2021092300
Q12021092602
median2021092606
Q32021092610
95-th percentile2021092700
Maximum2021092700
Range400
Interquartile range (IQR)8

Descriptive statistics

Standard deviation76.16287875
Coefficient of variation (CV)3.768401258 × 10-8
Kurtosis10.07169537
Mean2021092594
Median Absolute Deviation (MAD)4
Skewness-3.159388939
Sum2.2673122 × 1015
Variance5800.7841
MonotonicityIncreasing
2022-11-02T12:06:59.311930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
202109261093334
 
8.3%
202109260483352
 
7.4%
202109260079212
 
7.1%
202109260577050
 
6.9%
202109260674957
 
6.7%
202109260274405
 
6.6%
202109261173899
 
6.6%
202109270067390
 
6.0%
202109260766654
 
5.9%
202109260165090
 
5.8%
Other values (6)366482
32.7%
ValueCountFrequency (%)
202109230064584
5.8%
202109260079212
7.1%
202109260165090
5.8%
202109260274405
6.6%
202109260360513
5.4%
202109260483352
7.4%
202109260577050
6.9%
202109260674957
6.7%
202109260766654
5.9%
202109260855867
5.0%
ValueCountFrequency (%)
202109270067390
6.0%
202109261364446
5.7%
202109261259754
5.3%
202109261173899
6.6%
202109261093334
8.3%
202109260961318
5.5%
202109260855867
5.0%
202109260766654
5.9%
202109260674957
6.7%
202109260577050
6.9%

playId
Real number (ℝ≥0)

Distinct1000
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2155.017796
Minimum54
Maximum4928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 MiB
2022-11-02T12:06:59.433190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile247
Q11111
median2173
Q33174
95-th percentile4038
Maximum4928
Range4874
Interquartile range (IQR)2063

Descriptive statistics

Standard deviation1215.226313
Coefficient of variation (CV)0.5639054655
Kurtosis-1.119652312
Mean2155.017796
Median Absolute Deviation (MAD)1029
Skewness0.01527711883
Sum2417552839
Variance1476774.993
MonotonicityNot monotonic
2022-11-02T12:06:59.561529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27344669
 
0.4%
1004186
 
0.4%
15474140
 
0.4%
11743588
 
0.3%
35443565
 
0.3%
553335
 
0.3%
17993266
 
0.3%
2323197
 
0.3%
15363174
 
0.3%
36103105
 
0.3%
Other values (990)1085600
96.8%
ValueCountFrequency (%)
542507
0.2%
553335
0.3%
751564
0.1%
761817
0.2%
77690
 
0.1%
78828
 
0.1%
791012
 
0.1%
80713
 
0.1%
97897
 
0.1%
981541
0.1%
ValueCountFrequency (%)
4928943
0.1%
4883989
0.1%
4793736
0.1%
47671012
0.1%
4694759
0.1%
46701288
0.1%
46311058
0.1%
45831012
0.1%
45481380
0.1%
4526966
0.1%

nflId
Real number (ℝ≥0)

MISSING

Distinct1162
Distinct (%)0.1%
Missing48775
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean45749.33891
Minimum25511
Maximum54038
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 MiB
2022-11-02T12:06:59.690453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25511
5-th percentile37266
Q142471
median45281
Q348027
95-th percentile53480
Maximum54038
Range28527
Interquartile range (IQR)5556

Descriptive statistics

Standard deviation4993.095966
Coefficient of variation (CV)0.1091402867
Kurtosis0.1305421809
Mean45749.33891
Median Absolute Deviation (MAD)2807
Skewness-0.2086622686
Sum4.909132811 × 1010
Variance24931007.33
MonotonicityNot monotonic
2022-11-02T12:06:59.813661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
478102309
 
0.2%
478612309
 
0.2%
429242309
 
0.2%
524262309
 
0.2%
524472309
 
0.2%
534712309
 
0.2%
433802309
 
0.2%
534722220
 
0.2%
448232152
 
0.2%
535052152
 
0.2%
Other values (1152)1050363
93.6%
(Missing)48775
 
4.3%
ValueCountFrequency (%)
255112010
0.2%
289632112
0.2%
295501283
0.1%
298511043
0.1%
30842169
 
< 0.1%
308691203
0.1%
330841620
0.1%
331071400
0.1%
33130473
 
< 0.1%
33131987
0.1%
ValueCountFrequency (%)
5403846
 
< 0.1%
54006628
0.1%
539571335
0.1%
5394651
 
< 0.1%
53935194
 
< 0.1%
53930253
 
< 0.1%
53876260
 
< 0.1%
5368759
 
< 0.1%
5368177
 
< 0.1%
53679430
 
< 0.1%

frameId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct203
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.06156843
Minimum1
Maximum203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 MiB
2022-11-02T12:06:59.949467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median22
Q333
95-th percentile53
Maximum203
Range202
Interquartile range (IQR)22

Descriptive statistics

Standard deviation17.5924006
Coefficient of variation (CV)0.7311410583
Kurtosis11.61714996
Mean24.06156843
Median Absolute Deviation (MAD)11
Skewness2.109047558
Sum26992869
Variance309.492559
MonotonicityNot monotonic
2022-11-02T12:07:00.079035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126243
 
2.3%
1226243
 
2.3%
2126243
 
2.3%
2026243
 
2.3%
1926243
 
2.3%
1826243
 
2.3%
1726243
 
2.3%
1626243
 
2.3%
1526243
 
2.3%
1426243
 
2.3%
Other values (193)859395
76.6%
ValueCountFrequency (%)
126243
2.3%
226243
2.3%
326243
2.3%
426243
2.3%
526243
2.3%
626243
2.3%
726243
2.3%
826243
2.3%
926243
2.3%
1026243
2.3%
ValueCountFrequency (%)
20323
< 0.1%
20223
< 0.1%
20123
< 0.1%
20023
< 0.1%
19923
< 0.1%
19823
< 0.1%
19723
< 0.1%
19623
< 0.1%
19523
< 0.1%
19423
< 0.1%

time
Categorical

HIGH CARDINALITY

Distinct45093
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size8.6 MiB
2021-09-26T18:49:46.600
 
92
2021-09-26T18:49:45.700
 
92
2021-09-26T18:49:46.700
 
92
2021-09-26T18:49:46.500
 
92
2021-09-26T18:49:46.400
 
92
Other values (45088)
1121365 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters25801975
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row2021-09-24T00:23:08.400
2nd row2021-09-24T00:23:08.500
3rd row2021-09-24T00:23:08.600
4th row2021-09-24T00:23:08.700
5th row2021-09-24T00:23:08.800

Common Values

ValueCountFrequency (%)
2021-09-26T18:49:46.60092
 
< 0.1%
2021-09-26T18:49:45.70092
 
< 0.1%
2021-09-26T18:49:46.70092
 
< 0.1%
2021-09-26T18:49:46.50092
 
< 0.1%
2021-09-26T18:49:46.40092
 
< 0.1%
2021-09-26T18:49:46.30092
 
< 0.1%
2021-09-26T18:49:46.20092
 
< 0.1%
2021-09-26T18:49:46.10092
 
< 0.1%
2021-09-26T18:49:45.90092
 
< 0.1%
2021-09-26T18:49:45.80092
 
< 0.1%
Other values (45083)1120905
99.9%

Length

2022-11-02T12:07:00.198446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-09-26t18:49:46.60092
 
< 0.1%
2021-09-26t18:49:45.60092
 
< 0.1%
2021-09-26t18:49:45.70092
 
< 0.1%
2021-09-26t18:49:44.80092
 
< 0.1%
2021-09-26t18:49:44.90092
 
< 0.1%
2021-09-26t18:49:45.00092
 
< 0.1%
2021-09-26t18:49:45.10092
 
< 0.1%
2021-09-26t18:49:45.20092
 
< 0.1%
2021-09-26t18:49:45.30092
 
< 0.1%
2021-09-26t18:49:45.40092
 
< 0.1%
Other values (45083)1120905
99.9%

Most occurring characters

ValueCountFrequency (%)
05548083
21.5%
24550134
17.6%
12534807
9.8%
-2243650
8.7%
:2243650
8.7%
91679966
 
6.5%
61267162
 
4.9%
T1121825
 
4.3%
.1121825
 
4.3%
4792097
 
3.1%
Other values (4)2698776
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number19071025
73.9%
Other Punctuation3365475
 
13.0%
Dash Punctuation2243650
 
8.7%
Uppercase Letter1121825
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05548083
29.1%
24550134
23.9%
12534807
13.3%
91679966
 
8.8%
61267162
 
6.6%
4792097
 
4.2%
3783704
 
4.1%
5727030
 
3.8%
7595447
 
3.1%
8592595
 
3.1%
Other Punctuation
ValueCountFrequency (%)
:2243650
66.7%
.1121825
33.3%
Dash Punctuation
ValueCountFrequency (%)
-2243650
100.0%
Uppercase Letter
ValueCountFrequency (%)
T1121825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common24680150
95.7%
Latin1121825
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
05548083
22.5%
24550134
18.4%
12534807
10.3%
-2243650
9.1%
:2243650
9.1%
91679966
 
6.8%
61267162
 
5.1%
.1121825
 
4.5%
4792097
 
3.2%
3783704
 
3.2%
Other values (3)1915072
 
7.8%
Latin
ValueCountFrequency (%)
T1121825
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII25801975
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05548083
21.5%
24550134
17.6%
12534807
9.8%
-2243650
8.7%
:2243650
8.7%
91679966
 
6.5%
61267162
 
4.9%
T1121825
 
4.3%
.1121825
 
4.3%
4792097
 
3.1%
Other values (4)2698776
10.5%

jerseyNumber
Real number (ℝ≥0)

MISSING

Distinct99
Distinct (%)< 0.1%
Missing48775
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean49.36259913
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 MiB
2022-11-02T12:07:00.310328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q122
median52
Q375
95-th percentile96
Maximum99
Range98
Interquartile range (IQR)53

Descriptive statistics

Standard deviation30.03068304
Coefficient of variation (CV)0.6083691614
Kurtosis-1.351223707
Mean49.36259913
Median Absolute Deviation (MAD)27
Skewness0.05531355981
Sum52968537
Variance901.8419237
MonotonicityNot monotonic
2022-11-02T12:07:00.437911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2323753
 
2.1%
1122666
 
2.0%
7621637
 
1.9%
2421452
 
1.9%
2621168
 
1.9%
2120328
 
1.8%
9718448
 
1.6%
2218255
 
1.6%
2518168
 
1.6%
7417811
 
1.6%
Other values (89)869364
77.5%
(Missing)48775
 
4.3%
ValueCountFrequency (%)
112799
1.1%
217746
1.6%
38424
0.8%
410856
1.0%
56116
 
0.5%
69145
0.8%
79385
0.8%
811150
1.0%
97668
0.7%
1015901
1.4%
ValueCountFrequency (%)
9915805
1.4%
9813902
1.2%
9718448
1.6%
9611776
1.0%
959901
0.9%
9416225
1.4%
939563
0.9%
926501
 
0.6%
9116844
1.5%
9014304
1.3%

team
Categorical

HIGH CORRELATION

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 MiB
football
 
48775
MIA
 
44638
LV
 
44638
LAC
 
39864
KC
 
39864
Other values (28)
904046 

Length

Max length8
Median length3
Mean length2.958409734
Min length2

Characters and Unicode

Total characters3318818
Distinct characters30
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHOU
2nd rowHOU
3rd rowHOU
4th rowHOU
5th rowHOU

Common Values

ValueCountFrequency (%)
football48775
 
4.3%
MIA44638
 
4.0%
LV44638
 
4.0%
LAC39864
 
3.6%
KC39864
 
3.6%
BUF37884
 
3.4%
WAS37884
 
3.4%
NE36850
 
3.3%
NO36850
 
3.3%
NYG35849
 
3.2%
Other values (23)718729
64.1%

Length

2022-11-02T12:07:00.554481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
football48775
 
4.3%
mia44638
 
4.0%
lv44638
 
4.0%
lac39864
 
3.6%
kc39864
 
3.6%
buf37884
 
3.4%
was37884
 
3.4%
ne36850
 
3.3%
no36850
 
3.3%
nyg35849
 
3.2%
Other values (23)718729
64.1%

Most occurring characters

ValueCountFrequency (%)
A378741
 
11.4%
N282095
 
8.5%
I255992
 
7.7%
L254639
 
7.7%
C204754
 
6.2%
E188188
 
5.7%
T165374
 
5.0%
B139634
 
4.2%
D123860
 
3.7%
o97550
 
2.9%
Other values (20)1227991
37.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2928618
88.2%
Lowercase Letter390200
 
11.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A378741
12.9%
N282095
 
9.6%
I255992
 
8.7%
L254639
 
8.7%
C204754
 
7.0%
E188188
 
6.4%
T165374
 
5.6%
B139634
 
4.8%
D123860
 
4.2%
S97284
 
3.3%
Other values (14)838057
28.6%
Lowercase Letter
ValueCountFrequency (%)
o97550
25.0%
l97550
25.0%
f48775
12.5%
a48775
12.5%
b48775
12.5%
t48775
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin3318818
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A378741
 
11.4%
N282095
 
8.5%
I255992
 
7.7%
L254639
 
7.7%
C204754
 
6.2%
E188188
 
5.7%
T165374
 
5.0%
B139634
 
4.2%
D123860
 
3.7%
o97550
 
2.9%
Other values (20)1227991
37.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3318818
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A378741
 
11.4%
N282095
 
8.5%
I255992
 
7.7%
L254639
 
7.7%
C204754
 
6.2%
E188188
 
5.7%
T165374
 
5.0%
B139634
 
4.2%
D123860
 
3.7%
o97550
 
2.9%
Other values (20)1227991
37.0%

playDirection
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 MiB
left
610650 
right
511175 

Length

Max length5
Median length4
Mean length4.455663762
Min length4

Characters and Unicode

Total characters4998475
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowright
2nd rowright
3rd rowright
4th rowright
5th rowright

Common Values

ValueCountFrequency (%)
left610650
54.4%
right511175
45.6%

Length

2022-11-02T12:07:00.652594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-02T12:07:00.748536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
left610650
54.4%
right511175
45.6%

Most occurring characters

ValueCountFrequency (%)
t1121825
22.4%
l610650
12.2%
e610650
12.2%
f610650
12.2%
r511175
10.2%
i511175
10.2%
g511175
10.2%
h511175
10.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4998475
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t1121825
22.4%
l610650
12.2%
e610650
12.2%
f610650
12.2%
r511175
10.2%
i511175
10.2%
g511175
10.2%
h511175
10.2%

Most occurring scripts

ValueCountFrequency (%)
Latin4998475
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t1121825
22.4%
l610650
12.2%
e610650
12.2%
f610650
12.2%
r511175
10.2%
i511175
10.2%
g511175
10.2%
h511175
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4998475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t1121825
22.4%
l610650
12.2%
e610650
12.2%
f610650
12.2%
r511175
10.2%
i511175
10.2%
g511175
10.2%
h511175
10.2%

x
Real number (ℝ)

Distinct11794
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.24778826
Minimum-3.55
Maximum119.73
Zeros1
Zeros (%)< 0.1%
Negative40
Negative (%)< 0.1%
Memory size8.6 MiB
2022-11-02T12:07:00.843482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.55
5-th percentile19.27
Q139.59
median58.8
Q378.45
95-th percentile100.49
Maximum119.73
Range123.28
Interquartile range (IQR)38.86

Descriptive statistics

Standard deviation24.83586816
Coefficient of variation (CV)0.4191864184
Kurtosis-0.8132372129
Mean59.24778826
Median Absolute Deviation (MAD)19.42
Skewness0.05339164463
Sum66465650.07
Variance616.8203474
MonotonicityNot monotonic
2022-11-02T12:07:00.976990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.82238
 
< 0.1%
69.61232
 
< 0.1%
72.12216
 
< 0.1%
33.7213
 
< 0.1%
55.19212
 
< 0.1%
55.16205
 
< 0.1%
54.83201
 
< 0.1%
51.02201
 
< 0.1%
69.51200
 
< 0.1%
69.6198
 
< 0.1%
Other values (11784)1119709
99.8%
ValueCountFrequency (%)
-3.551
< 0.1%
-3.541
< 0.1%
-3.531
< 0.1%
-3.51
< 0.1%
-3.481
< 0.1%
-3.461
< 0.1%
-3.42
< 0.1%
-3.331
< 0.1%
-3.281
< 0.1%
-3.251
< 0.1%
ValueCountFrequency (%)
119.732
< 0.1%
119.724
< 0.1%
119.72
< 0.1%
119.692
< 0.1%
119.663
< 0.1%
119.631
 
< 0.1%
119.622
< 0.1%
119.613
< 0.1%
119.64
< 0.1%
119.591
 
< 0.1%

y
Real number (ℝ)

Distinct5400
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.62128333
Minimum-1.8
Maximum56.32
Zeros1
Zeros (%)< 0.1%
Negative61
Negative (%)< 0.1%
Memory size8.6 MiB
2022-11-02T12:07:01.110227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1.8
5-th percentile11.24
Q121.83
median26.59
Q331.4
95-th percentile42.05
Maximum56.32
Range58.12
Interquartile range (IQR)9.57

Descriptive statistics

Standard deviation8.363867646
Coefficient of variation (CV)0.3141797314
Kurtosis0.3265031168
Mean26.62128333
Median Absolute Deviation (MAD)4.79
Skewness0.0191207113
Sum29864421.17
Variance69.954282
MonotonicityNot monotonic
2022-11-02T12:07:01.391260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.861186
 
0.1%
23.851133
 
0.1%
23.821116
 
0.1%
23.771111
 
0.1%
23.761104
 
0.1%
23.81103
 
0.1%
23.871083
 
0.1%
23.841073
 
0.1%
23.811070
 
0.1%
23.791063
 
0.1%
Other values (5390)1110783
99.0%
ValueCountFrequency (%)
-1.81
< 0.1%
-1.751
< 0.1%
-1.651
< 0.1%
-1.621
< 0.1%
-1.511
< 0.1%
-1.481
< 0.1%
-1.471
< 0.1%
-1.312
< 0.1%
-1.281
< 0.1%
-1.122
< 0.1%
ValueCountFrequency (%)
56.321
< 0.1%
55.721
< 0.1%
55.121
< 0.1%
55.091
< 0.1%
55.021
< 0.1%
54.891
< 0.1%
54.81
< 0.1%
54.751
< 0.1%
54.591
< 0.1%
54.461
< 0.1%

s
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2180
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.579950625
Minimum0
Maximum28.17
Zeros69592
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size8.6 MiB
2022-11-02T12:07:01.520755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median2.12
Q33.81
95-th percentile6.8
Maximum28.17
Range28.17
Interquartile range (IQR)3.06

Descriptive statistics

Standard deviation2.396155797
Coefficient of variation (CV)0.928760331
Kurtosis14.4549856
Mean2.579950625
Median Absolute Deviation (MAD)1.49
Skewness2.366121315
Sum2894253.11
Variance5.741562602
MonotonicityNot monotonic
2022-11-02T12:07:01.638074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
069592
 
6.2%
0.0116384
 
1.5%
0.029518
 
0.8%
0.037175
 
0.6%
0.045997
 
0.5%
0.055123
 
0.5%
0.064794
 
0.4%
0.074548
 
0.4%
0.084232
 
0.4%
0.093902
 
0.3%
Other values (2170)990560
88.3%
ValueCountFrequency (%)
069592
6.2%
0.0116384
 
1.5%
0.029518
 
0.8%
0.037175
 
0.6%
0.045997
 
0.5%
0.055123
 
0.5%
0.064794
 
0.4%
0.074548
 
0.4%
0.084232
 
0.4%
0.093902
 
0.3%
ValueCountFrequency (%)
28.171
< 0.1%
28.071
< 0.1%
28.061
< 0.1%
27.921
< 0.1%
27.821
< 0.1%
27.751
< 0.1%
27.651
< 0.1%
27.481
< 0.1%
27.471
< 0.1%
27.341
< 0.1%

a
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1535
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.781462144
Minimum0
Maximum27.8
Zeros65085
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size8.6 MiB
2022-11-02T12:07:01.763168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.71
median1.53
Q32.57
95-th percentile4.45
Maximum27.8
Range27.8
Interquartile range (IQR)1.86

Descriptive statistics

Standard deviation1.429146826
Coefficient of variation (CV)0.8022324977
Kurtosis5.958978856
Mean1.781462144
Median Absolute Deviation (MAD)0.91
Skewness1.405267039
Sum1998488.77
Variance2.042460649
MonotonicityNot monotonic
2022-11-02T12:07:01.879582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
065085
 
5.8%
0.0112683
 
1.1%
0.027332
 
0.7%
0.035547
 
0.5%
0.044592
 
0.4%
0.054143
 
0.4%
1.063640
 
0.3%
1.183622
 
0.3%
0.963576
 
0.3%
0.063562
 
0.3%
Other values (1525)1008043
89.9%
ValueCountFrequency (%)
065085
5.8%
0.0112683
 
1.1%
0.027332
 
0.7%
0.035547
 
0.5%
0.044592
 
0.4%
0.054143
 
0.4%
0.063562
 
0.3%
0.073219
 
0.3%
0.082885
 
0.3%
0.092579
 
0.2%
ValueCountFrequency (%)
27.81
< 0.1%
27.771
< 0.1%
27.751
< 0.1%
27.361
< 0.1%
27.271
< 0.1%
25.751
< 0.1%
24.851
< 0.1%
24.321
< 0.1%
24.191
< 0.1%
24.091
< 0.1%

dis
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct547
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2612968957
Minimum0
Maximum7.42
Zeros70615
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size8.6 MiB
2022-11-02T12:07:02.006979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.08
median0.21
Q30.38
95-th percentile0.68
Maximum7.42
Range7.42
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2564520855
Coefficient of variation (CV)0.9814586004
Kurtosis49.41449584
Mean0.2612968957
Median Absolute Deviation (MAD)0.15
Skewness4.196383183
Sum293129.39
Variance0.06576767217
MonotonicityNot monotonic
2022-11-02T12:07:02.133170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
070615
 
6.3%
0.0159895
 
5.3%
0.0234957
 
3.1%
0.0326970
 
2.4%
0.0423688
 
2.1%
0.0521702
 
1.9%
0.1820885
 
1.9%
0.220685
 
1.8%
0.1720682
 
1.8%
0.1920665
 
1.8%
Other values (537)801081
71.4%
ValueCountFrequency (%)
070615
6.3%
0.0159895
5.3%
0.0234957
3.1%
0.0326970
 
2.4%
0.0423688
 
2.1%
0.0521702
 
1.9%
0.0620523
 
1.8%
0.0720322
 
1.8%
0.0819807
 
1.8%
0.0919766
 
1.8%
ValueCountFrequency (%)
7.421
< 0.1%
7.161
< 0.1%
6.991
< 0.1%
6.531
< 0.1%
6.481
< 0.1%
6.331
< 0.1%
6.321
< 0.1%
6.041
< 0.1%
5.981
< 0.1%
5.951
< 0.1%

o
Real number (ℝ≥0)

MISSING

Distinct36001
Distinct (%)3.4%
Missing48775
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean178.8167407
Minimum0
Maximum360
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.6 MiB
2022-11-02T12:07:02.268955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.57
Q188.77
median177.93
Q3268.4
95-th percentile329.72
Maximum360
Range360
Interquartile range (IQR)179.63

Descriptive statistics

Standard deviation99.32762729
Coefficient of variation (CV)0.5554716348
Kurtosis-1.355980466
Mean178.8167407
Median Absolute Deviation (MAD)89.82
Skewness0.01258116145
Sum191879303.6
Variance9865.977542
MonotonicityNot monotonic
2022-11-02T12:07:02.391509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
901257
 
0.1%
87.34111
 
< 0.1%
89111
 
< 0.1%
266.42101
 
< 0.1%
95.23100
 
< 0.1%
85.4898
 
< 0.1%
262.7498
 
< 0.1%
258.3198
 
< 0.1%
259.698
 
< 0.1%
90.8198
 
< 0.1%
Other values (35991)1070880
95.5%
(Missing)48775
 
4.3%
ValueCountFrequency (%)
03
 
< 0.1%
0.0115
< 0.1%
0.0221
< 0.1%
0.0312
< 0.1%
0.0413
< 0.1%
0.0516
< 0.1%
0.0615
< 0.1%
0.0722
< 0.1%
0.0824
< 0.1%
0.0915
< 0.1%
ValueCountFrequency (%)
36011
 
< 0.1%
359.9911
 
< 0.1%
359.9810
 
< 0.1%
359.9716
< 0.1%
359.9617
< 0.1%
359.9511
 
< 0.1%
359.9430
< 0.1%
359.9317
< 0.1%
359.9222
< 0.1%
359.9121
< 0.1%

dir
Real number (ℝ≥0)

MISSING

Distinct36001
Distinct (%)3.4%
Missing48775
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean180.5803742
Minimum0
Maximum360
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.6 MiB
2022-11-02T12:07:02.525933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24.53
Q190.99
median180.51
Q3270.5
95-th percentile336.23
Maximum360
Range360
Interquartile range (IQR)179.51

Descriptive statistics

Standard deviation100.7337335
Coefficient of variation (CV)0.5578332306
Kurtosis-1.285046983
Mean180.5803742
Median Absolute Deviation (MAD)89.75
Skewness-0.001276817356
Sum193771770.6
Variance10147.28507
MonotonicityNot monotonic
2022-11-02T12:07:02.651847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92.4373
 
< 0.1%
274.373
 
< 0.1%
271.7872
 
< 0.1%
269.4372
 
< 0.1%
270.6172
 
< 0.1%
91.5171
 
< 0.1%
87.0271
 
< 0.1%
88.171
 
< 0.1%
91.3371
 
< 0.1%
270.4970
 
< 0.1%
Other values (35991)1072334
95.6%
(Missing)48775
 
4.3%
ValueCountFrequency (%)
013
< 0.1%
0.0117
< 0.1%
0.0227
< 0.1%
0.0323
< 0.1%
0.0426
< 0.1%
0.0521
< 0.1%
0.0625
< 0.1%
0.0728
< 0.1%
0.0816
< 0.1%
0.0928
< 0.1%
ValueCountFrequency (%)
36010
 
< 0.1%
359.9931
< 0.1%
359.9832
< 0.1%
359.9728
< 0.1%
359.9628
< 0.1%
359.9524
< 0.1%
359.9414
< 0.1%
359.9323
< 0.1%
359.9222
< 0.1%
359.9121
< 0.1%

event
Categorical

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 MiB
None
1037116 
ball_snap
 
26197
pass_forward
 
22701
autoevent_ballsnap
 
11638
autoevent_passforward
 
11454
Other values (15)
 
12719

Length

Max length25
Median length4
Mean length4.669379805
Min length3

Characters and Unicode

Total characters5238227
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None1037116
92.4%
ball_snap26197
 
2.3%
pass_forward22701
 
2.0%
autoevent_ballsnap11638
 
1.0%
autoevent_passforward11454
 
1.0%
play_action5405
 
0.5%
run1725
 
0.2%
qb_sack1518
 
0.1%
pass_arrived1127
 
0.1%
autoevent_passinterrupted644
 
0.1%
Other values (10)2300
 
0.2%

Length

2022-11-02T12:07:02.778438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none1037116
92.4%
ball_snap26197
 
2.3%
pass_forward22701
 
2.0%
autoevent_ballsnap11638
 
1.0%
autoevent_passforward11454
 
1.0%
play_action5405
 
0.5%
run1725
 
0.2%
qb_sack1518
 
0.1%
pass_arrived1127
 
0.1%
autoevent_passinterrupted644
 
0.1%
Other values (10)2300
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n1108968
21.2%
o1102597
21.0%
e1088958
20.8%
N1037116
19.8%
a184414
 
3.5%
s113482
 
2.2%
_83559
 
1.6%
l81765
 
1.6%
p80937
 
1.5%
r73991
 
1.4%
Other values (15)282440
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4117552
78.6%
Uppercase Letter1037116
 
19.8%
Connector Punctuation83559
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n1108968
26.9%
o1102597
26.8%
e1088958
26.4%
a184414
 
4.5%
s113482
 
2.8%
l81765
 
2.0%
p80937
 
2.0%
r73991
 
1.8%
t56971
 
1.4%
b39629
 
1.0%
Other values (13)185840
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
N1037116
100.0%
Connector Punctuation
ValueCountFrequency (%)
_83559
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5154668
98.4%
Common83559
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n1108968
21.5%
o1102597
21.4%
e1088958
21.1%
N1037116
20.1%
a184414
 
3.6%
s113482
 
2.2%
l81765
 
1.6%
p80937
 
1.6%
r73991
 
1.4%
t56971
 
1.1%
Other values (14)225469
 
4.4%
Common
ValueCountFrequency (%)
_83559
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5238227
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n1108968
21.2%
o1102597
21.0%
e1088958
20.8%
N1037116
19.8%
a184414
 
3.5%
s113482
 
2.2%
_83559
 
1.6%
l81765
 
1.6%
p80937
 
1.5%
r73991
 
1.4%
Other values (15)282440
 
5.4%

Interactions

2022-11-02T12:06:50.662672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:12.474230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:15.874587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:19.355911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:22.769659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:26.167432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:29.687881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:33.147268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:36.700977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:40.117976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:43.574863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:47.166962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:50.960384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:12.760018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:16.149309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:19.642796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:23.061652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:26.613503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:29.979775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:33.442225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:36.986736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:40.411058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:43.860528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:47.461629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:51.245920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:13.038545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:16.426613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:19.926569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:23.340631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:26.889708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:30.254709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:33.731762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:37.275985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:40.696977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:44.154915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:47.748031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:51.541813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:13.316308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:16.702245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:20.214899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:23.614938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:27.168011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:30.540006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:34.013386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:37.562007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:40.987901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:44.442045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:48.042439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:51.837167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:13.602023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:16.982027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:20.511765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:23.903819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:27.445647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:30.824446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:34.296875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:37.855251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:41.284220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:44.733753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:48.336139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:52.125705image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:13.885581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:17.258721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:20.788740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:24.189249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:27.718535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:31.096743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:34.580296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:38.136530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:41.573937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:45.182163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:48.622845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:52.413496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:14.170243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:17.670965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:21.067856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:24.472138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:27.994428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:31.375784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:34.853712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:38.421059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:41.860313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:45.461856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:48.912726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:52.704395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:14.449272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:17.949520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:21.347737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:24.752908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:28.270761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:31.660175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:35.125120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:38.693348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:42.141709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:45.735893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:49.200692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:52.997217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:14.728168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:18.225863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:21.627306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:25.028267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:28.546619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:31.956565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:35.404333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:38.971547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:42.415844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:46.011414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:49.486809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:53.293085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:15.008776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:18.499976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:21.912710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:25.300281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:28.829648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:32.266361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:35.846345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:39.245234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:42.694058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:46.282696image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:49.779121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:53.586115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:15.300626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:18.784443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:22.200890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:25.586356image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:29.120935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:32.560647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:36.127595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:39.539200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:42.993724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:46.573421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:50.072557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:53.877780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:15.590077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:19.075171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:22.489608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:25.874294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:29.402741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:32.849936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:36.412804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:39.829302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:43.296257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:46.867383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:06:50.365033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-02T12:07:02.878407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-02T12:07:03.029792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-02T12:07:03.175538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-02T12:07:03.321897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-02T12:07:03.456787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-02T12:07:03.565093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-02T12:06:54.630146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-02T12:06:55.900746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-02T12:06:57.771531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-02T12:06:58.472849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

gameIdplayIdnflIdframeIdtimejerseyNumberteamplayDirectionxysadisodirevent
020210923005441300.012021-09-24T00:23:08.40058.0HOUright38.6628.980.000.000.00259.88205.34None
120210923005441300.022021-09-24T00:23:08.50058.0HOUright38.6628.980.000.000.01259.88197.10None
220210923005441300.032021-09-24T00:23:08.60058.0HOUright38.6628.970.000.000.00259.88192.98None
320210923005441300.042021-09-24T00:23:08.70058.0HOUright38.6628.970.020.340.00259.88181.68None
420210923005441300.052021-09-24T00:23:08.80058.0HOUright38.6628.970.050.370.00260.78199.16None
520210923005441300.062021-09-24T00:23:08.90058.0HOUright38.6528.970.100.620.01262.87271.88autoevent_ballsnap
620210923005441300.072021-09-24T00:23:09.00058.0HOUright38.6328.980.251.140.02264.34287.33None
720210923005441300.082021-09-24T00:23:09.10058.0HOUright38.5929.000.481.730.05265.65293.21ball_snap
820210923005441300.092021-09-24T00:23:09.20058.0HOUright38.5229.020.752.470.07266.51293.93None
920210923005441300.0102021-09-24T00:23:09.30058.0HOUright38.4229.061.172.820.11268.87290.62None

Last rows

gameIdplayIdnflIdframeIdtimejerseyNumberteamplayDirectionxysadisodirevent
112181520210927004156NaN382021-09-28T03:24:35.400NaNfootballleft76.2326.884.723.010.43NaNNaNNone
112181620210927004156NaN392021-09-28T03:24:35.500NaNfootballleft75.8827.365.130.920.59NaNNaNNone
112181720210927004156NaN402021-09-28T03:24:35.600NaNfootballleft75.5427.905.621.410.63NaNNaNNone
112181820210927004156NaN412021-09-28T03:24:35.700NaNfootballleft75.2028.425.541.040.63NaNNaNNone
112181920210927004156NaN422021-09-28T03:24:35.800NaNfootballleft74.9028.865.112.070.53NaNNaNrun
112182020210927004156NaN432021-09-28T03:24:35.900NaNfootballleft74.6129.305.114.880.52NaNNaNNone
112182120210927004156NaN442021-09-28T03:24:36.000NaNfootballleft74.3529.714.665.330.49NaNNaNNone
112182220210927004156NaN452021-09-28T03:24:36.100NaNfootballleft74.1230.094.164.690.44NaNNaNNone
112182320210927004156NaN462021-09-28T03:24:36.200NaNfootballleft73.1530.525.153.221.06NaNNaNNone
112182420210927004156NaN472021-09-28T03:24:36.300NaNfootballleft72.3530.845.233.340.86NaNNaNNone